SoroushVahidi/scidocs huggingface dataset:
Predicting Facial Beauty without Landmarks
(2010)

A fundamental task in artificial intelligence and computer vision is to build machines that can behave like a human in recognizing a broad range of visual concepts. This paper aims to investigate and develop intelligent systems for learning the concept of female facial beauty and producing human-like predictors. Artists and social scientists have long been fascinated by the notion of facial beauty, but study by computer scientists has only begun in the last few years. Our work is notably different from and goes beyond previous works in several aspects: 1) we focus on fully-automatic learning approaches that do not require costly manual annotation of landmark facial features but simply take the raw pixels as inputs; 2) our study is based on a collection of data that is an order of magnitude larger than that of any previous study; 3) we imposed no restrictions in terms of pose, lighting, background, expression, age, and ethnicity on the face images used for training and testing. These factors significantly increased the difficulty of the learning task. We show that a biologically-inspired model with multiple layers of trainable feature extractors can produce results that are much more human-like than the previously used eigenface approach. Finally, we develop a novel visualization method to interpret the learned model and revealed the existence of several beautiful features that go beyond the current averageness and symmetry hypotheses.
Distributed Hessian-Free Optimization for Deep Neural Network
(2017)

Training deep neural network is a high dimensional and a highly non-convex optimization problem. In this paper, we revisit Hessian-free optimization method for deep networks with negative curvature direction detection. We also develop its distributed variant and demonstrate superior scaling potential to SGD, which allows more efficiently utilizing larger computing resources thus enabling large models and faster time to obtain desired solution. We show that these techniques accelerate the training process for both the standard MNIST dataset and also the TIMIT speech recognition problem, demonstrating robust performance with upto an order of magnitude larger batch sizes. This increased scaling potential is illustrated with near l inear speed-up on upto 32 CPU nodes for a simple 4-layer network.
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
(2019)

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into  $K$  orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.
Multiple memory systems as substrates for multiple decision systems
(2015)

It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an "internal model." Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. Here we investigated the relationship between model-based RL and relational memory by comparing individual differences across behavioral tasks designed to measure either capacity. Human subjects performed two tasks, a learning and generalization task (acquired equivalence) which involves relational encoding and depends on the hippocampus; and a sequential RL task that could be solved by either a model-based or model-free strategy. We assessed the correlation between subjects' use of flexible, relational memory, as measured by generalization in the acquired equivalence task, and their differential reliance on either RL strategy in the decision task. We observed a significant positive relationship between generalization and model-based, but not model-free, choice strategies. These results are consistent with the hypothesis that model-based RL, like acquired equivalence, relies on a more general-purpose relational memory system.
Mass detection on mammogram images : A first assessment of deep learning techniques
(2013)

Deep Learning approaches have gathered a lot of attention lately. In this work, we study their application to the breast cancer field, in particular for mass detection in mammograms. Several experiments were made on a real mammogram benchmark dataset. Deep Learning approaches were compared to other classification methodologies. It was concluded that, although useful, the implementation used does not outperform SVMs. Further study and adjustment of the method for this application is needed.
Open-set Plant Identification Using an Ensemble of Deep Convolutional Neural Networks
(2016)

Open-set recognition, a challenging problem in computer vision, is concerned with identification or verification tasks where queries may belong to unknown classes. This work describes a fine-grained plant identification system consisting of an ensemble of deep convolutional neural networks, within an open-set identification framework. Two wellknown deep learning architectures of VGGNet and GoogLeNet, pretrained on the object recognition dataset of ILSVRC 2012, are fine-tuned using the plant dataset of LifeCLEF 2015. Moreover, GoogLeNet is finetuned using plant and non-plant images, to serve for rejecting samples from non-plant classes. Our systems have been evaluated on the test dataset of PlantCLEF 2016 by the campaign organizers and the best proposed model has achieved an official score of 0.738 in terms of the mean average precision, while the best official score was announced to be 0.742.
Real time hand pose estimation using depth sensors
(2011)

This paper describes a depth image based real-time skeleton fitting algorithm for the hand, using an object recognition by parts approach, and the use of this hand modeler in an American Sign Language (ASL) digit recognition application. In particular, we created a realistic 3D hand model that represents the hand with 21 different parts. Random decision forests (RDF) are trained on synthetic depth images generated by animating the hand model, which are then used to perform per pixel classification and assign each pixel to a hand part. The classification results are fed into a local mode finding algorithm to estimate the joint locations for the hand skeleton. The system can process depth images retrieved from Kinect in real-time at 30 fps. As an application of the system, we also describe a support vector machine (SVM) based recognition module for the ten digits of ASL based on our method, which attains a recognition rate of 99.9% on live depth images in real-time1.
A Microstrip Ultra-Wideband Bandpass Filter With Cascaded Broadband Bandpass and Bandstop Filters
(2007)

This paper develops a novel ultra-wideband bandpass filter by cascading a broadband bandpass filter with another broadband bandstop filter. Properly selected impedances of transmission lines achieve broadband bandpass and bandstop filters and make independent designs possible. Detailed design and synthesis procedures are provided; moreover, agreement between measured and theoretically predicted results demonstrates feasibility of the proposed filter. Due to its simple structure, the ultra-wideband bandpass filter newly introduced in this paper is suitable for integration in the single-chipped circuit or implementation on printed circuit boards.
A Review of Window Query Processing for Data Streams
(2013)

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control. Category: Ubiquitous computing
Current-mode sensorless control of single-phase brushless DC fan motors
(2011)

This paper proposes a new control scheme for the implementation of a low-cost and high efficiency sensorless speed control IC for single-phase brushless dc (BLDC) fan motors. The proposed control scheme detects the zero-crossing-point (ZCP) of the measured back-EMF to generate commutation signals without Hall sensor. A current mode soft switching scheme is used to smooth the current spikes induced by the reduction of the back-EMF when the rotor is crossing the commutation boundary. An adaptive blanking time control strategy is used to adjust the time interval to ensure correct detection of the ZCP. An open-loop constant Ampere/Hertz ramping control scheme is developed for the startup control from zero speed and switching to sensorless mode once the ZCP is detected. The proposed sensorless control scheme has been verified by using computer simulation based on a developed single-phase BLDC fan motor model. Experimental verification of the proposed control scheme has been carried out by using digital implementation technique. Experimental results reveal the superior performance of the proposed control scheme.
High-Power Pulse Generator With Flexible Output Pattern
(2010)

This paper presents a high-voltage bipolar rectangular pulse generator using a solid-state boosting front-end and an H-bridge output stage. The topology generates rectangular pulses with fast enough rise time and allows easy step-up input voltage. In addition, the circuit is able to adjust positive or negative pulsewidth, dead time between two pulses, and operating frequency. The topology can also be controlled to produce unipolar pulses and other pulse patterns without changing its configuration. With an appropriate dc source, the output voltage can also be adjusted to requirements of different applications. The intended application for such a circuit is algal cell membrane rupture for oil extraction, although additional applications, include biotechnology and plasma sciences, medicine, and food industry. A 1 kV/200 A bipolar solid-state pulse generator was fabricated to validate the theoretical analysis presented in this paper. In addition, to validate the analysis with simulations and prototype tests, biological test were conducted in order to examine the technical value of the proposed circuit. These evaluations seem to suggest that oil production rate from bipolar pulses may double that of an equivalent process with unipolar pulses.
X CLAIM : Interoperability with Cryptocurrency-Backed Tokens ?
(2018)

Building trustless cross-blockchain trading protocols is challenging. Therefore, centralized liquidity providers remain the preferred route to execute transfers across chains — which fundamentally contradicts the purpose of permissionless ledgers to replace trusted intermediaries. Enabling crossblockchain trades could not only enable currently competing blockchain projects to better collaborate, but seems of particular importance to decentralized exchanges as those are currently limited to the trade of digital assets within their respective blockchain ecosystem. In this paper we systematize the notion of cryptocurrencybacked tokens, an approach towards trustless cross-chain communication. We propose XCLAIM, a protocol for issuing, trading, and redeeming e.g. Bitcoin-backed tokens on Ethereum. We provide implementations for three possible protocol versions and evaluate their security and on-chain costs. With XCLAIM, it costs at most USD 1.17 to issue an arbitrary amount of Bitcoinbacked tokens on Ethereum, given current blockchain transaction fees. Our protocol requires no modifications to Bitcoin’s and Ethereum’s consensus rules and is general enough to support other cryptocurrencies.
Reading comprehension and its underlying components in second-language learners: A meta-analysis of studies comparing first- and second-language learners.
(2014)

We report a systematic meta-analytic review of studies comparing reading comprehension and its underlying components (language comprehension, decoding, and phonological awareness) in first- and second-language learners. The review included 82 studies, and 576 effect sizes were calculated for reading comprehension and underlying components. Key findings were that, compared to first-language learners, second-language learners display a medium-sized deficit in reading comprehension (pooled effect size d = -0.62), a large deficit in language comprehension (pooled effect size d = -1.12), but only small differences in phonological awareness (pooled effect size d = -0.08) and decoding (pooled effect size d = -0.12). A moderator analysis showed that characteristics related to the type of reading comprehension test reliably explained the variation in the differences in reading comprehension between first- and second-language learners. For language comprehension, studies of samples from low socioeconomic backgrounds and samples where only the first language was used at home generated the largest group differences in favor of first-language learners. Test characteristics and study origin reliably contributed to the variations between the studies of language comprehension. For decoding, Canadian studies showed group differences in favor of second-language learners, whereas the opposite was the case for U.S. studies. Regarding implications, unless specific decoding problems are detected, interventions that aim to ameliorate reading comprehension problems among second-language learners should focus on language comprehension skills.
Exploring the factors associated with Web site success in the context of electronic commerce
(2000)

Web sites are being widely deployed commercially. As the widespread use and dependency on Web technology increases, so does the need to assess factors associated with Web site success. The objective is to explore these factors in the context of electronic commerce (EC). The research framework was derived from information systems and marketing literature. Webmasters from Fortune 1000 companies were used as the target group for a survey. Four factors that are critical to Web site success in EC were identi®ed: (1) information and service quality, (2) system use, (3) playfulness, and (4) system design quality. An analysis of the data provides valuable managerial implications for Web site success in the context of electronic commerce. # 2000 Elsevier Science B.V. All rights reserved.
Web Site Usability, Design, and Performance Metrics
(2002)

Websites provide the key interface for consumer use of the Internet. This research reports on a series of three studies that develop and validate Web site usability, design and performance metrics, including download delay, navigability, site content, interactivity, and responsiveness. The performance metric that was developed includes the subconstructs user satisfaction, the likelihood of return, and the frequency of use. Data was collected in 1997, 1999, and 2000 from corporate Web sites via three methods, namely, a jury, third-party ratings, and a software agent. Significant associations betweenWeb site design elements and Web site performance indicate that the constructs demonstrate good nomological validity. Together, the three studies provide a set of measures with acceptable validity and reliability. The findings also suggest lack of significant common methods biases across the jury-collected data, third-party data, and agent-collected data. Results suggest that Web site success is a first-order construct. Moreover, Web site success is significantly associated with Web site download delay (speed of access and display rate within the Web site), navigation (organization, arrangement, layout, and sequencing), content (amount and variety of product information), interactivity (customization and interactivity), and responsiveness (feedback options and FAQs). (e-Commerce, Web Metrics, or Measurement;Web Site Usability;Design and Performance Constructs; Construct Validity; Nomological Validity)
Data Integration: After the Teenage Years
(2017)

The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the landscape of data integration since the work on rewriting queries using views in the mid-1990's. In addition, we describe two important challenges for the field going forward. The first challenge is to develop good open-source tools for different components of data integration pipelines. The second challenge is to provide practitioners with viable solutions for the long-standing problem of systematically combining structured and unstructured data.
A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons.
(1997)

The role of back-propagating dendritic action potentials in the induction of long-term potentiation (LTP) was investigated in CA1 neurons by means of dendritic patch recordings and simultaneous calcium imaging. Pairing of subthreshold excitatory postsynaptic potentials (EPSPs) with back-propagating action potentials resulted in an amplification of dendritic action potentials and evoked calcium influx near the site of synaptic input. This pairing also induced a robust LTP, which was reduced when EPSPs were paired with non-back-propagating action potentials or when stimuli were unpaired. Action potentials thus provide a synaptically controlled, associative signal to the dendrites for Hebbian modifications of synaptic strength.
Screening for trisomy 18 by maternal age, fetal nuchal translucency, free beta-human chorionic gonadotropin and pregnancy-associated plasma protein-A.
(2008)

OBJECTIVES
To derive a model and examine the performance of first-trimester screening for trisomy 18 by maternal age, fetal nuchal translucency (NT) thickness, and maternal serum free beta-human chorionic gonadotropin (beta-hCG) and pregnancy-associated plasma protein-A (PAPP-A).


METHODS
Prospective combined screening for trisomy 21 was performed at 11 + 0 to 13 + 6 weeks in 56 893 singleton pregnancies, including 56 376 cases of euploid fetuses, 395 with trisomy 21 and 122 with trisomy 18. The measured free beta-hCG and PAPP-A were converted into a multiple of the median (MoM) and then into likelihood ratios (LR). Similarly, the measured NT was transformed into LRs using the mixture model of NT distributions. In each case the LRs for NT and the biochemical markers were multiplied by the age and gestation-related risk to derive the risk for trisomy 21 and trisomy 18. Detection rates (DRs) and false-positive rates (FPRs) were calculated by taking the proportions with risks above a given risk threshold.


RESULTS
In screening with the algorithm for trisomy 21, at a FPR of 3%, the estimated DRs of trisomies 21 and 18 were 89% and 82%, respectively. The use of an algorithm for trisomy 18 identified 93% of affected fetuses at a FPR of 0.2%. When the algorithm for trisomy 21 was used and screen positivity was fixed at a FPR of 3%, and in addition the algorithm for trisomy 18 was used and screen positivity was fixed at a FPR of 0.2%, the overall FPR was 3.1% and the DRs of trisomies 21 and 18 were 90% and 97%, respectively.


CONCLUSIONS
A beneficial side effect of first-trimester combined screening for trisomy 21 is the detection of a high proportion of fetuses with trisomy 18. If an algorithm for trisomy 18 in addition to the one for trisomy 21 is used, more than 95% of trisomy 18 fetuses can be detected with a minor increase of 0.1% in the overall FPR.
Classification and adulteration detection of vegetable oils based on fatty acid profiles.
(2014)

The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (principal component analysis and hierarchical clustering analysis), supervised (random forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by random forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.